Multi-Step Bayesian Optimization for One-Dimensional Feasibility Determination
Bayesian optimization methods allocate limited sampling budgets to maximize expensive-to-evaluate functions. One-step-lookahead policies are often used, but computing optimal multi-step-lookahead policies remains a challenge. We consider a specialized Bayesian optimization problem: finding the superlevel set of an expensive one-dimensional function, with a Markov process prior. We compute the Bayes-optimal sampling policy efficiently, and characterize the suboptimality of one-step lookahead. Our numerical experiments demonstrate that the one-step lookahead policy is close to optimal in this problem, performing within 98% of optimal in the experimental settings considered.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian OptimizationSimilar Papers 제목 키워드 기반
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization
Conventional methods for Bayesian optimization (BO) primarily involve one-step optimal decisions (e.g., maximizing expected improvement of the next step). To avoid myopic behavior, multi-step lookahead BO algorithms such…
Bayesian OptimizationDecision MakingHyperparameter OptimizationReinforcement Learning (RL)+1Feasibility-Driven Trust Region Bayesian Optimization
Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulations or experiments. However, many of thes…
Bayesian OptimizationRandom Postprocessing for Combinatorial Bayesian Optimization
Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objective function in interest, resulting in man…
Bayesian OptimizationHigh dimensional Bayesian Optimization Algorithm for Complex System in Time Series
At present, high-dimensional global optimization problems with time-series models have received much attention from engineering fields. Since it was proposed, Bayesian optimization has quickly become a popular and promis…
Bayesian OptimizationDimensionality Reductionglobal-optimizationTime Series+2Practical Two-Step Lookahead Bayesian Optimization
Expected improvement and other acquisition functions widely used in Bayesian optimization use a "one-step" assumption: they value objective function evaluations assuming no future evaluations will be performed. Because w…
Bayesian OptimizationVocal Bursts Valence Prediction